Linear Regression with Python

Linear Regression with Python

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1. Simple Linear Regression

Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.

What is Linear Regression

Finding the Parameters

Building Linear Regression Using NumPy

Building Linear Regression Using Statsmodels

Predict House Prices

2. Multiple Linear Regression

Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.

Linear Regression with Two Features

Linear Regression with n Features

Building Multiple Linear Regression

Choosing the Features

Predict Prices Using Two Features

3. Polynomial Regression

A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.

Quadratic Regression

Polynomial Regression

Building Polynomial Regression

Interpolation vs Extrapolation

Evaluate the Model

4. Choosing The Best Model

Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.

Predict Prices Using Polynomial Regression

Зміст курсу

Linear Regression with Python

Linear Regression with Python

1. Simple Linear Regression

What is Linear Regression Finding the Parameters Building Linear Regression Using NumPy Building Linear Regression Using Statsmodels Predict House Prices

2. Multiple Linear Regression

Linear Regression with Two Features Linear Regression with n Features Building Multiple Linear Regression Choosing the Features Predict Prices Using Two Features

3. Polynomial Regression

Quadratic Regression Polynomial Regression Building Polynomial Regression Interpolation vs Extrapolation Evaluate the Model

4. Choosing The Best Model

Metrics Overfitting R-squared Predict Prices Using Polynomial Regression

Predict Prices Using Two Features

For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age and square_feet).


              1234
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv')
print(df.head())

Your task is to build a Multiple Linear Regression model using the OLS class. Also, you will print the summary table to look at the p-values of each feature.

Завдання

Swipe to show code editor

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 2. Розділ 5

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Predict Prices Using Two Features

For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age and square_feet).


              1234
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv')
print(df.head())

Your task is to build a Multiple Linear Regression model using the OLS class. Also, you will print the summary table to look at the p-values of each feature.

Завдання

Swipe to show code editor

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 2. Розділ 5

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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